Effective interprofessional teams: “Contact is not enough” to build a team
Bibliographic record
Abstract
INTRODUCTION: Teamwork and interprofessional practice and learning are becoming integral to health care. It is anticipated that these approaches can maximize professional resources and optimize patient care. Current research, however, suggests that primary health care teams may lack the capacity to function at a level that enhances the individual contributions of their members and team effectiveness. This study explores perceptions of effective primary health care teams to determine the related learning needs of primary health care professionals. METHODS: Primary health care team members with a particular interest in teamwork shared perspectives of effective teamwork and educational needs in interprofessional focus groups. Transcripts from nine focus groups with a total of 61 participants were analyzed using content analysis and grounded hermeneutic approaches to identify themes. RESULTS: Five themes of primary care team effectiveness emerged: (1) understanding and respecting team members' roles, (2) recognizing that teams require work, (3) understanding primary health care, (4) working together: practical "know-how" for sharing patient care, and (5) communication. Communication was identified as the essential factor in effective primary health care teams. DISCUSSION: Several characteristics of effective primary health care teams and the related knowledge and skills that professionals require as effective team members are identified. Effective teamwork requires specific cognitive, technical, and affective competence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".